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Infect Genet Evol. 2017 Sep;53:15-23. doi: 10.1016/j.meegid.2017.05.007. Epub 2017 May 9.

Exploring resistance pathways for first-generation NS3/4A protease inhibitors boceprevir and telaprevir using Bayesian network learning.

Author information

1
KU Leuven, University of Leuven, Department of Microbiology and Immunology, Rega Institute for Medical Research, Clinical and Epidemiological Virology, Herestraat 49, box 1040, 3000 Leuven, Belgium. Electronic address: lize.cuypers@kuleuven.be.
2
KU Leuven, University of Leuven, Department of Microbiology and Immunology, Rega Institute for Medical Research, Clinical and Epidemiological Virology, Herestraat 49, box 1040, 3000 Leuven, Belgium; Artificial Intelligence Lab, Vrije Universiteit Brussel, Pleinlaan 2, 1050 Brussels, Belgium. Electronic address: pieter.libin@vub.ac.be.
3
KU Leuven, University of Leuven, Department of Microbiology and Immunology, Rega Institute for Medical Research, Clinical and Epidemiological Virology, Herestraat 49, box 1040, 3000 Leuven, Belgium. Electronic address: yoeri.schrooten@uzleuven.be.
4
KU Leuven, University of Leuven, Department of Microbiology and Immunology, Rega Institute for Medical Research, Clinical and Epidemiological Virology, Herestraat 49, box 1040, 3000 Leuven, Belgium. Electronic address: kristof.theys@kuleuven.be.
5
Department of Experimental Medicine and Surgery, University of Rome "Tor Vergata", Rome, Italy. Electronic address: di.maio@med.uniroma2.it.
6
Department of Experimental Medicine and Surgery, University of Rome "Tor Vergata", Rome, Italy. Electronic address: valeria.cento@uniroma2.it.
7
Institute of Microbiology and Immunology, Faculty of Medicine, University of Ljubljana, Ljubljana, Slovenia. Electronic address: maja.lunar@mf.uni-lj.si.
8
University Hospitals Leuven, Department of Hepatology, Herestraat 49, 3000 Leuven, Belgium. Electronic address: frederik.nevens@uzleuven.be.
9
Institute of Microbiology and Immunology, Faculty of Medicine, University of Ljubljana, Ljubljana, Slovenia. Electronic address: mario.poljak@mf.uni-lj.si.
10
Department of Experimental Medicine and Surgery, University of Rome "Tor Vergata", Rome, Italy. Electronic address: ceccherini@med.uniroma2.it.
11
Artificial Intelligence Lab, Vrije Universiteit Brussel, Pleinlaan 2, 1050 Brussels, Belgium. Electronic address: ann.nowe@como.vub.ac.be.
12
KU Leuven, University of Leuven, Department of Microbiology and Immunology, Rega Institute for Medical Research, Clinical and Epidemiological Virology, Herestraat 49, box 1040, 3000 Leuven, Belgium. Electronic address: kristel.vanlaethem@uzleuven.be.
13
KU Leuven, University of Leuven, Department of Microbiology and Immunology, Rega Institute for Medical Research, Clinical and Epidemiological Virology, Herestraat 49, box 1040, 3000 Leuven, Belgium; Center for Global Health and Tropical Medicine, Microbiology Unit, Institute for Hygiene and Tropical Medicine, University Nova de Lisboa, Rua da Junqueira 100, 1349-008 Lisbon, Portugal. Electronic address: annemie.vandamme@uzleuven.be.

Abstract

Resistance-associated variants (RAVs) have been shown to influence treatment response to direct-acting antivirals (DAAs) and first generation NS3/4A protease inhibitors (PIs) in particular. Interpretation of hepatitis C virus (HCV) genotypic drug resistance remains a challenge, especially in patients who previously failed DAA therapy and need to be retreated with a second DAA based regimen. Bayesian network (BN) learning on HCV sequence data from patients treated with DAAs could provide insight in resistance pathways against PIs for HCV subtypes 1a and 1b, in a similar way as applied before for HIV. The publicly available 'Rega-BN' tool chain was developed to study associative analyses for various pathogens. Our first analysis, comparing sequences from PI-naïve and PI-experienced patients, determined that NS3 substitutions R155K and V36M arise with PI-exposure in HCV1a infected patients, and were defined as major and minor resistance-associated variants respectively. NS3 variant 174H was newly identified as potentially related to PI resistance. In a second analysis, NS3 sequences from PI-naïve patients who cleared the virus during PI therapy and from PI-naïve patients who failed PI therapy were compared, showing that NS3 baseline variant 67S predisposes to treatment-failure and variant 72I to treatment success. This approach has the potential to better characterize the role of more RAVs, if sufficient therapy annotated sequence data becomes available in curated public databases. In addition, polymorphisms present in baseline sequences that predispose patients to therapy failure can be identified using this approach.

KEYWORDS:

Bayesian network learning; Drug resistance; HCV; NS3/4A protease inhibitors

PMID:
28499845
DOI:
10.1016/j.meegid.2017.05.007
[Indexed for MEDLINE]

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